1. Field of the Invention
This invention relates to computer vision, and more particularly to an apparatus and method for improved object recognition based on local surface geometries calculated directly from relative range images.
2. Description of the Related Art
In recent years there has been considerable activity in understanding range images. Much of the activity has been devoted to working with indoor range finders, such as light stripe finders, which do not encounter a rollover problem because they are absolute range finders, or are working with very short distances. The remaining activity has focused on using outdoor range finders which encounter the rollover problem. These outdoor range finders have been somewhat successful only for very short distances because of rollover. These range finders concentrate working in just one band of range and therefore fail to address the rollover effect.
Rollover is particularly a problem for long distance sensing. It is especially evident when trying to segment an image into objects and background for recognizing objects using LADAR images. Segmentation into figure and ground, or target and clutter, is easier in LADAR images because of the availability of depth measurements. Depth also helps in resolving occlusion and in describing object shapes which aids significantly in object recognition. For example, objects may be segmented into parts and the parts could be described in terms of surface properties. These surface properties can then be used in object recognition models which furthers accurate object recognition.
LADAR images have rollover in depth due to inherent limitations of the precision of the hardware (for example, 8 bits of precision gives rollover every 256 units of depth) or due to the physics of the way the sensor measures depth. For example, depth measurement by phase causes rollover depending on the wavelength of the modulating wave used by the sensor. Regardless of the cause, bands of relative range result in each sensed image where each range band is equal to one rollover.
There are at least two problems attributed to rollover. The first involves object-background separation, also known as segmentation. Segmentation can become quite difficult due to the rollover effect. The situation often exists where objects "straddle" across range bands. When this happens, the sensor reports the same object as possibly located two, or more, different distances from the camera. This makes it difficult to separate the object from the background and accurately describe the scene.
The second problem caused by rollover is that the sensor may also report different objects as having the same distance from the camera when they may actually be one or more rollovers apart in distance and therefore actually not equal distances from the camera. This situation also creates difficulties in segmentation and accurate description of the scene.
One approach currently used for solving these problems involves converting a relative range image to an "absolute" range image, once in absolute range, rollover is not a problem. The specific approach obtains range values at all points in the image with respect to some base point in the image and then shifts the range values at range discontinuities that are estimated to be rollover boundaries. The resultant image is then segmented for figure and ground.
There are several problems with this approach. The main problem is that one must assume the segmentation into object and background corrects for rollover. In other words, the method assumes the segmentation solution, then solves for the rollover and finally solves for actual segmentation. The problems created by the assumption that the segmentation corrects for rollover are particularly acute when objects occupy several range bands. It is generally not possible to solve this problem automatically without first separating the objects from the background. Since this separation is the desired solution, one encounters the proverbial circular problem. At best such an approach can be performed by hand; it is very difficult to implement in a program for autonomous machine vision.
Other problems with the above approach include using more bits per pixel in the image due to correcting the range to absolute range. This results in greater memory requirements and longer computation time. Also, as more bits are used, the contrast in depth (ratio of the values of two pixels) provided in the new image is less than that in the original image. Thus, features which have poor contrast with the background may be more difficult to detect in the respective absolute range image. If, on the other hand, the absolute range image is represented in terms of the same number of bits as the relative range image, the resolution of the absolute range image is decreased. Again, this makes feature extraction difficult.